IEEE BigData 2023 Keystroke Verification Challenge (KVC)
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arXiv
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| Format: | Preprint |
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2024
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| author | Stragapede, Giuseppe Vera-Rodriguez, Ruben Tolosana, Ruben Morales, Aythami DeAndres-Tame, Ivan Damer, Naser Fierrez, Julian Garcia, Javier-Ortega Gonzalez, Nahuel Shadrikov, Andrei Gordin, Dmitrii Schmitt, Leon Wimmer, Daniel Grossmann, Christoph Krieger, Joerdis Heinz, Florian Krestel, Ron Mayer, Christoffer Haberl, Simon Gschrey, Helena Yamagishi, Yosuke Saha, Sanjay Rasnayaka, Sanka Wickramanayake, Sandareka Sim, Terence Gutfeter, Weronika Baran, Adam Krzyszton, Mateusz Jaskola, Przemyslaw |
| author_facet | Stragapede, Giuseppe Vera-Rodriguez, Ruben Tolosana, Ruben Morales, Aythami DeAndres-Tame, Ivan Damer, Naser Fierrez, Julian Garcia, Javier-Ortega Gonzalez, Nahuel Shadrikov, Andrei Gordin, Dmitrii Schmitt, Leon Wimmer, Daniel Grossmann, Christoph Krieger, Joerdis Heinz, Florian Krestel, Ron Mayer, Christoffer Haberl, Simon Gschrey, Helena Yamagishi, Yosuke Saha, Sanjay Rasnayaka, Sanka Wickramanayake, Sandareka Sim, Terence Gutfeter, Weronika Baran, Adam Krzyszton, Mateusz Jaskola, Przemyslaw |
| contents | This paper describes the results of the IEEE BigData 2023 Keystroke Verification Challenge (KVC), that considers the biometric verification performance of Keystroke Dynamics (KD), captured as tweet-long sequences of variable transcript text from over 185,000 subjects. The data are obtained from two of the largest public databases of KD up to date, the Aalto Desktop and Mobile Keystroke Databases, guaranteeing a minimum amount of data per subject, age and gender annotations, absence of corrupted data, and avoiding excessively unbalanced subject distributions with respect to the considered demographic attributes. Several neural architectures were proposed by the participants, leading to global Equal Error Rates (EERs) as low as 3.33% and 3.61% achieved by the best team respectively in the desktop and mobile scenario, outperforming the current state of the art biometric verification performance for KD. Hosted on CodaLab, the KVC will be made ongoing to represent a useful tool for the research community to compare different approaches under the same experimental conditions and to deepen the knowledge of the field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_16559 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | IEEE BigData 2023 Keystroke Verification Challenge (KVC) Stragapede, Giuseppe Vera-Rodriguez, Ruben Tolosana, Ruben Morales, Aythami DeAndres-Tame, Ivan Damer, Naser Fierrez, Julian Garcia, Javier-Ortega Gonzalez, Nahuel Shadrikov, Andrei Gordin, Dmitrii Schmitt, Leon Wimmer, Daniel Grossmann, Christoph Krieger, Joerdis Heinz, Florian Krestel, Ron Mayer, Christoffer Haberl, Simon Gschrey, Helena Yamagishi, Yosuke Saha, Sanjay Rasnayaka, Sanka Wickramanayake, Sandareka Sim, Terence Gutfeter, Weronika Baran, Adam Krzyszton, Mateusz Jaskola, Przemyslaw Computer Vision and Pattern Recognition This paper describes the results of the IEEE BigData 2023 Keystroke Verification Challenge (KVC), that considers the biometric verification performance of Keystroke Dynamics (KD), captured as tweet-long sequences of variable transcript text from over 185,000 subjects. The data are obtained from two of the largest public databases of KD up to date, the Aalto Desktop and Mobile Keystroke Databases, guaranteeing a minimum amount of data per subject, age and gender annotations, absence of corrupted data, and avoiding excessively unbalanced subject distributions with respect to the considered demographic attributes. Several neural architectures were proposed by the participants, leading to global Equal Error Rates (EERs) as low as 3.33% and 3.61% achieved by the best team respectively in the desktop and mobile scenario, outperforming the current state of the art biometric verification performance for KD. Hosted on CodaLab, the KVC will be made ongoing to represent a useful tool for the research community to compare different approaches under the same experimental conditions and to deepen the knowledge of the field. |
| title | IEEE BigData 2023 Keystroke Verification Challenge (KVC) |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.16559 |